Bibliographic record
Abstract
To ensure optimal accuracy of motor behaviour across development and ageing, the relationship between sensory input and motor output must be calibrated, a process called sensorimotor adaptation. It has long been thought that sensorimotor adaptation is driven by cerebellar-based sensory prediction errors (i.e., mismatch between predicted and actual sensory consequences of movement). More recently, there has been increasing support for the possibility that target errors (i.e., missing the intended target) also contribute to adaptation. In spite of considerable behavioural and modeling work, the neural mechanisms involved in the processing of these different types of errors remain unclear. In this light, a recent focus of our lab has been to characterize the neocortical manifestations of prediction errors and target errors in the context of reach adaptation using electroencephalography (EEG). I will first present data showing that the parietal response to visual reafferent feedback from the moving limb is increased when the timing or direction of feedback is experimentally manipulated, suggesting that parietal areas contribute to the processing of prediction errors. I will then present results showing that oscillatory power in the theta-band (4-7 Hz) over mid-frontal regions is increased following target errors, and more so when they are associated with monetary punishments. Overall, this work identifies distinct markers of prediction errors and target errors during sensorimotor adaptation, providing possible targets for neurostimulation approaches destined to optimize human motor learning and performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".